Traffic forecasting predicts future traffic, given a sequence of historical traffic observations like speed, density, and volume detected by sensors on a road network. In this case, \(M\)  previous time-steps in a sequence are used to predict \(H\)  future time-steps. It is important to encode the continuity and periodicity of time-series data and capture the spatio-temporal dependencies in traffic forecasting. The Traffic Transformer [1]} has been built based on two existing networks, the first being a Graph Neural Network [2]}, [3]} followed by the Transformer as shown in Figure REF . The Transformer models the temporal dependencies, and the Graph Neural Network is used to model spatial dependencies.
<FIGURE>